Overview

Dataset statistics

Number of variables13
Number of observations172
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.6 KiB
Average record size in memory104.7 B

Variable types

Numeric13

Alerts

log10Epeak has constant value "-inf" Constant
Fbest is highly correlated with T_abest and 1 other fieldsHigh correlation
T_abest is highly correlated with FbestHigh correlation
Photonindex_plateau is highly correlated with Beta and 1 other fieldsHigh correlation
Beta is highly correlated with Photonindex_plateau and 1 other fieldsHigh correlation
Gamma is highly correlated with Photonindex_plateau and 1 other fieldsHigh correlation
log10PeakFlux is highly correlated with Fbest and 1 other fieldsHigh correlation
log10T90 is highly correlated with log10FluenceHigh correlation
log10Fluence is highly correlated with log10PeakFlux and 1 other fieldsHigh correlation
Fbest is highly correlated with T_abest and 1 other fieldsHigh correlation
T_abest is highly correlated with FbestHigh correlation
Photonindex_plateau is highly correlated with Beta and 1 other fieldsHigh correlation
Beta is highly correlated with Photonindex_plateau and 1 other fieldsHigh correlation
Gamma is highly correlated with Photonindex_plateau and 1 other fieldsHigh correlation
log10PeakFlux is highly correlated with Fbest and 1 other fieldsHigh correlation
log10T90 is highly correlated with log10FluenceHigh correlation
log10Fluence is highly correlated with log10PeakFlux and 1 other fieldsHigh correlation
Fbest is highly correlated with T_abestHigh correlation
T_abest is highly correlated with FbestHigh correlation
Photonindex_plateau is highly correlated with BetaHigh correlation
Beta is highly correlated with Photonindex_plateauHigh correlation
Fbest is highly correlated with T_abest and 5 other fieldsHigh correlation
T_abest is highly correlated with Fbest and 2 other fieldsHigh correlation
Photonindex_plateau is highly correlated with Fbest and 2 other fieldsHigh correlation
Beta is highly correlated with Fbest and 2 other fieldsHigh correlation
Gamma is highly correlated with Fbest and 4 other fieldsHigh correlation
log10PeakFlux is highly correlated with Fbest and 1 other fieldsHigh correlation
log10T90 is highly correlated with T_abest and 2 other fieldsHigh correlation
log10Fluence is highly correlated with Fbest and 3 other fieldsHigh correlation
log10z is highly correlated with Gamma and 2 other fieldsHigh correlation
log10Epeak has 172 (100.0%) infinite values Infinite
Unnamed: 0 has unique values Unique
T_abest has unique values Unique
Alpha_best has unique values Unique
Gamma has unique values Unique

Reproduction

Analysis started2021-10-30 03:14:42.933512
Analysis finished2021-10-30 03:14:57.815651
Duration14.88 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

UNIQUE

Distinct172
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.5116279
Minimum0
Maximum206
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2021-10-30T12:14:57.876190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.55
Q152.25
median100.5
Q3150.5
95-th percentile196.45
Maximum206
Range206
Interquartile range (IQR)98.25

Descriptive statistics

Standard deviation60.38962077
Coefficient of variation (CV)0.5949034807
Kurtosis-1.167204525
Mean101.5116279
Median Absolute Deviation (MAD)50
Skewness0.03339284403
Sum17460
Variance3646.906297
MonotonicityStrictly increasing
2021-10-30T12:14:57.977540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.6%
1381
 
0.6%
1301
 
0.6%
1311
 
0.6%
1321
 
0.6%
1331
 
0.6%
1341
 
0.6%
1351
 
0.6%
1361
 
0.6%
1371
 
0.6%
Other values (162)162
94.2%
ValueCountFrequency (%)
01
0.6%
11
0.6%
21
0.6%
31
0.6%
41
0.6%
51
0.6%
61
0.6%
71
0.6%
81
0.6%
91
0.6%
ValueCountFrequency (%)
2061
0.6%
2051
0.6%
2041
0.6%
2031
0.6%
2021
0.6%
2001
0.6%
1991
0.6%
1981
0.6%
1971
0.6%
1961
0.6%

Fbest
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct171
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-10.66097639
Minimum-12.6568
Maximum-8.315649473
Zeros0
Zeros (%)0.0%
Negative172
Negative (%)100.0%
Memory size1.5 KiB
2021-10-30T12:14:58.081367image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-12.6568
5-th percentile-11.86273308
Q1-11.28225898
median-10.7506
Q3-10.05239026
95-th percentile-9.1257002
Maximum-8.315649473
Range4.341150527
Interquartile range (IQR)1.22986872

Descriptive statistics

Standard deviation0.8340950752
Coefficient of variation (CV)-0.07823815048
Kurtosis-0.252515123
Mean-10.66097639
Median Absolute Deviation (MAD)0.597358755
Skewness0.3445467353
Sum-1833.687939
Variance0.6957145945
MonotonicityNot monotonic
2021-10-30T12:14:58.181096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-10.75272
 
1.2%
-11.37571
 
0.6%
-11.52081
 
0.6%
-9.504431
 
0.6%
-11.31171
 
0.6%
-9.848911
 
0.6%
-11.30951
 
0.6%
-10.61171
 
0.6%
-12.65681
 
0.6%
-10.7911
 
0.6%
Other values (161)161
93.6%
ValueCountFrequency (%)
-12.65681
0.6%
-12.33351
0.6%
-12.26561
0.6%
-12.23881
0.6%
-12.085373831
0.6%
-11.96111
0.6%
-11.920633191
0.6%
-11.88331
0.6%
-11.86331
0.6%
-11.862269231
0.6%
ValueCountFrequency (%)
-8.3156494731
0.6%
-8.60991
0.6%
-8.7134855381
0.6%
-8.821181
0.6%
-8.917321
0.6%
-9.0505894971
0.6%
-9.05721
0.6%
-9.0758831621
0.6%
-9.0823360011
0.6%
-9.161181
0.6%

T_abest
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct172
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.729446653
Minimum2.071571247
Maximum5.76634
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2021-10-30T12:14:58.283814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.071571247
5-th percentile2.630607237
Q13.262963597
median3.70097
Q34.150535
95-th percentile4.866125
Maximum5.76634
Range3.694768753
Interquartile range (IQR)0.887571403

Descriptive statistics

Standard deviation0.6471598761
Coefficient of variation (CV)0.1735270501
Kurtosis0.2232245622
Mean3.729446653
Median Absolute Deviation (MAD)0.44982
Skewness0.1962929184
Sum641.4648243
Variance0.4188159052
MonotonicityNot monotonic
2021-10-30T12:14:58.389555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.863561
 
0.6%
3.693231
 
0.6%
3.536861
 
0.6%
3.778241
 
0.6%
2.856081
 
0.6%
4.292221
 
0.6%
3.303371
 
0.6%
5.766341
 
0.6%
3.394311
 
0.6%
3.854191
 
0.6%
Other values (162)162
94.2%
ValueCountFrequency (%)
2.0715712471
0.6%
2.3119508541
0.6%
2.4551710161
0.6%
2.492171
0.6%
2.524291
0.6%
2.5416655481
0.6%
2.566061
0.6%
2.590061
0.6%
2.6110971941
0.6%
2.646571
0.6%
ValueCountFrequency (%)
5.766341
0.6%
5.4091904171
0.6%
5.2272947681
0.6%
5.10571
0.6%
5.0895207161
0.6%
5.085581
0.6%
5.033441
0.6%
4.911241
0.6%
4.869261
0.6%
4.863561
0.6%

Alpha_best
Real number (ℝ≥0)

UNIQUE

Distinct172
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.344641211
Minimum0.542155161
Maximum2.77382
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2021-10-30T12:14:58.501520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.542155161
5-th percentile0.888203065
Q11.1067075
median1.239855297
Q31.468921449
95-th percentile2.066925
Maximum2.77382
Range2.231664839
Interquartile range (IQR)0.3622139485

Descriptive statistics

Standard deviation0.3800360895
Coefficient of variation (CV)0.2826301071
Kurtosis2.167745197
Mean1.344641211
Median Absolute Deviation (MAD)0.1901073655
Skewness1.339924859
Sum231.2782883
Variance0.1444274293
MonotonicityNot monotonic
2021-10-30T12:14:58.606800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.108181
 
0.6%
0.8979421
 
0.6%
1.251341
 
0.6%
1.649811
 
0.6%
1.160561
 
0.6%
1.227231
 
0.6%
1.549591
 
0.6%
2.333221
 
0.6%
1.177051
 
0.6%
2.038081
 
0.6%
Other values (162)162
94.2%
ValueCountFrequency (%)
0.5421551611
0.6%
0.7741691
0.6%
0.8422171
0.6%
0.8631591
0.6%
0.868193711
0.6%
0.8716561
0.6%
0.8751711
0.6%
0.8837111
0.6%
0.8861058111
0.6%
0.8899191
0.6%
ValueCountFrequency (%)
2.773821
0.6%
2.646431
0.6%
2.5196037331
0.6%
2.50541
0.6%
2.387631
0.6%
2.333221
0.6%
2.325741
0.6%
2.2631273441
0.6%
2.102181
0.6%
2.038081
0.6%

Photonindex_plateau
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct113
Distinct (%)65.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.898589535
Minimum1.145
Maximum2.46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2021-10-30T12:14:58.792704image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.145
5-th percentile1.5255
Q11.77
median1.9
Q32.03
95-th percentile2.26725
Maximum2.46
Range1.315
Interquartile range (IQR)0.26

Descriptive statistics

Standard deviation0.2243488692
Coefficient of variation (CV)0.1181660728
Kurtosis0.9590699622
Mean1.898589535
Median Absolute Deviation (MAD)0.13
Skewness-0.3611707933
Sum326.5574
Variance0.05033241509
MonotonicityNot monotonic
2021-10-30T12:14:58.905548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.867
 
4.1%
1.985
 
2.9%
1.925
 
2.9%
2.034
 
2.3%
1.834
 
2.3%
2.064
 
2.3%
1.874
 
2.3%
1.844
 
2.3%
1.93
 
1.7%
1.913
 
1.7%
Other values (103)129
75.0%
ValueCountFrequency (%)
1.1451
0.6%
1.1981
0.6%
1.25291
0.6%
1.3751
0.6%
1.391
0.6%
1.3961
0.6%
1.471
0.6%
1.5021
0.6%
1.521
0.6%
1.531
0.6%
ValueCountFrequency (%)
2.461
0.6%
2.381
0.6%
2.3751
0.6%
2.361
0.6%
2.3571
0.6%
2.3171
0.6%
2.31
0.6%
2.291
0.6%
2.271
0.6%
2.2651
0.6%

Beta
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct112
Distinct (%)65.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8980953488
Minimum0.145
Maximum1.46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2021-10-30T12:14:59.012558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.145
5-th percentile0.547325
Q10.77
median0.9
Q31.03
95-th percentile1.26725
Maximum1.46
Range1.315
Interquartile range (IQR)0.26

Descriptive statistics

Standard deviation0.223389443
Coefficient of variation (CV)0.2487368889
Kurtosis0.9647015915
Mean0.8980953488
Median Absolute Deviation (MAD)0.13
Skewness-0.3343488423
Sum154.4724
Variance0.04990284325
MonotonicityNot monotonic
2021-10-30T12:14:59.125219image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.867
 
4.1%
0.984
 
2.3%
0.874
 
2.3%
0.94
 
2.3%
1.034
 
2.3%
1.064
 
2.3%
0.924
 
2.3%
0.834
 
2.3%
0.653
 
1.7%
0.953
 
1.7%
Other values (102)131
76.2%
ValueCountFrequency (%)
0.1451
0.6%
0.1981
0.6%
0.25291
0.6%
0.3751
0.6%
0.391
0.6%
0.3961
0.6%
0.5021
0.6%
0.521
0.6%
0.531
0.6%
0.56151
0.6%
ValueCountFrequency (%)
1.461
0.6%
1.381
0.6%
1.3751
0.6%
1.361
0.6%
1.3571
0.6%
1.3171
0.6%
1.31
0.6%
1.291
0.6%
1.271
0.6%
1.2651
0.6%

Gamma
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct172
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.98343907
Minimum1.37246
Maximum2.55463
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2021-10-30T12:14:59.231937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.37246
5-th percentile1.7059775
Q11.89189
median1.99125
Q32.075365
95-th percentile2.2418345
Maximum2.55463
Range1.18217
Interquartile range (IQR)0.183475

Descriptive statistics

Standard deviation0.1730739775
Coefficient of variation (CV)0.08725953833
Kurtosis1.647617819
Mean1.98343907
Median Absolute Deviation (MAD)0.095635
Skewness-0.1239664612
Sum341.15152
Variance0.0299546017
MonotonicityNot monotonic
2021-10-30T12:14:59.342294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.995441
 
0.6%
2.125511
 
0.6%
1.843041
 
0.6%
1.937721
 
0.6%
1.959961
 
0.6%
1.632181
 
0.6%
1.794961
 
0.6%
1.934471
 
0.6%
2.016781
 
0.6%
1.785471
 
0.6%
Other values (162)162
94.2%
ValueCountFrequency (%)
1.372461
0.6%
1.466041
0.6%
1.525121
0.6%
1.597881
0.6%
1.61771
0.6%
1.632181
0.6%
1.659341
0.6%
1.686841
0.6%
1.701111
0.6%
1.709961
0.6%
ValueCountFrequency (%)
2.554631
0.6%
2.49471
0.6%
2.457421
0.6%
2.371441
0.6%
2.295591
0.6%
2.288231
0.6%
2.258371
0.6%
2.257221
0.6%
2.249761
0.6%
2.235351
0.6%

log10PeakFlux
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct155
Distinct (%)90.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.678996572
Minimum-7.63638802
Maximum-5.07007044
Zeros0
Zeros (%)0.0%
Negative172
Negative (%)100.0%
Memory size1.5 KiB
2021-10-30T12:14:59.449399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-7.63638802
5-th percentile-7.470015479
Q1-7.083687845
median-6.777283529
Q3-6.32140147
95-th percentile-5.606400181
Maximum-5.07007044
Range2.56631758
Interquartile range (IQR)0.7622863751

Descriptive statistics

Standard deviation0.5429382506
Coefficient of variation (CV)-0.08129039217
Kurtosis-0.0763337807
Mean-6.678996572
Median Absolute Deviation (MAD)0.3413321893
Skewness0.6345763156
Sum-1148.78741
Variance0.294781944
MonotonicityNot monotonic
2021-10-30T12:14:59.559136image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-6.9030899874
 
2.3%
-6.9281179933
 
1.7%
-6.6968039433
 
1.7%
-7.3251388592
 
1.2%
-6.6655462492
 
1.2%
-6.5670307092
 
1.2%
-6.8013429132
 
1.2%
-6.6090648932
 
1.2%
-6.7772835292
 
1.2%
-6.6516951372
 
1.2%
Other values (145)148
86.0%
ValueCountFrequency (%)
-7.636388021
0.6%
-7.6345120151
0.6%
-7.5272435511
0.6%
-7.5171264161
0.6%
-7.5157001611
0.6%
-7.5142785741
0.6%
-7.4989407381
0.6%
-7.4881166391
0.6%
-7.4749551931
0.6%
-7.4659738941
0.6%
ValueCountFrequency (%)
-5.070070441
0.6%
-5.2358238681
0.6%
-5.3726341431
0.6%
-5.4190750241
0.6%
-5.5171264161
0.6%
-5.5243288121
0.6%
-5.5543957971
0.6%
-5.5638373531
0.6%
-5.5833594931
0.6%
-5.6252516541
0.6%

log10Epeak
Real number (ℝ)

CONSTANT
INFINITE
REJECTED

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite172
Infinite (%)100.0%
Mean-inf
Minimum-inf
Maximum-inf
Zeros0
Zeros (%)0.0%
Negative172
Negative (%)100.0%
Memory size1.5 KiB
2021-10-30T12:14:59.643820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-inf
5-th percentilenan
Q1nan
mediannan
Q3nan
95-th percentilenan
Maximum-inf
Rangenan
Interquartile range (IQR)nan

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean-inf
Median Absolute Deviation (MAD)nan
Skewnessnan
Sum-inf
Variancenan
MonotonicityIncreasing
2021-10-30T12:14:59.709580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
-inf172
100.0%
ValueCountFrequency (%)
-inf172
100.0%
ValueCountFrequency (%)
-inf172
100.0%

log10T90
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct160
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.561931328
Minimum-0.7447274949
Maximum3.146128036
Zeros0
Zeros (%)0.0%
Negative6
Negative (%)3.5%
Memory size1.5 KiB
2021-10-30T12:14:59.796375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-0.7447274949
5-th percentile0.1461280357
Q11.191673841
median1.681241237
Q32.052959277
95-th percentile2.465075198
Maximum3.146128036
Range3.890855531
Interquartile range (IQR)0.8612854361

Descriptive statistics

Standard deviation0.7144896613
Coefficient of variation (CV)0.4574398684
Kurtosis0.6575366907
Mean1.561931328
Median Absolute Deviation (MAD)0.4142598803
Skewness-0.84316446
Sum268.6521885
Variance0.5104954761
MonotonicityNot monotonic
2021-10-30T12:14:59.982630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.6812412374
 
2.3%
1.7481880272
 
1.2%
1.0413926852
 
1.2%
0.69897000432
 
1.2%
2.3222192952
 
1.2%
1.8061799742
 
1.2%
12
 
1.2%
-0.52287874532
 
1.2%
1.6646419762
 
1.2%
0.14612803572
 
1.2%
Other values (150)150
87.2%
ValueCountFrequency (%)
-0.74472749491
0.6%
-0.52287874532
1.2%
-0.32790214211
0.6%
-0.11918640771
0.6%
-0.091514981121
0.6%
0.079181246051
0.6%
0.11394335231
0.6%
0.14612803572
1.2%
0.18752072081
0.6%
0.20411998271
0.6%
ValueCountFrequency (%)
3.1461280361
0.6%
2.8512583491
0.6%
2.740283721
0.6%
2.6869935661
0.6%
2.642464521
0.6%
2.5314789171
0.6%
2.5003737141
0.6%
2.4771212551
0.6%
2.468347331
0.6%
2.4623979981
0.6%

log10Fluence
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct130
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.661021487
Minimum-7.167491087
Maximum-4.387216143
Zeros0
Zeros (%)0.0%
Negative172
Negative (%)100.0%
Memory size1.5 KiB
2021-10-30T12:15:00.091108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-7.167491087
5-th percentile-6.708994382
Q1-6.034033442
median-5.646939945
Q3-5.19599396
95-th percentile-4.684704725
Maximum-4.387216143
Range2.780274944
Interquartile range (IQR)0.8380394816

Descriptive statistics

Standard deviation0.6058593326
Coefficient of variation (CV)-0.10702297
Kurtosis-0.5006071286
Mean-5.661021487
Median Absolute Deviation (MAD)0.4292751133
Skewness-0.1816461647
Sum-973.6956957
Variance0.3670655309
MonotonicityNot monotonic
2021-10-30T12:15:00.190438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.8538719645
 
2.9%
-5.9586073155
 
2.9%
-5.6989700044
 
2.3%
-5.6197887584
 
2.3%
-5.4436974993
 
1.7%
-5.9208187543
 
1.7%
-5.7447274953
 
1.7%
-5.481486063
 
1.7%
-5.0457574913
 
1.7%
-6.1739251972
 
1.2%
Other values (120)137
79.7%
ValueCountFrequency (%)
-7.1674910871
0.6%
-7.0043648051
0.6%
-71
0.6%
-6.8538719642
1.2%
-6.7958800171
0.6%
-6.7695510791
0.6%
-6.7447274951
0.6%
-6.7212463991
0.6%
-6.6989700042
1.2%
-6.6595558851
0.6%
ValueCountFrequency (%)
-4.3872161431
0.6%
-4.4436974991
0.6%
-4.5287082891
0.6%
-4.5528419691
0.6%
-4.5783960731
0.6%
-4.6197887581
0.6%
-4.6575773191
0.6%
-4.6777807052
1.2%
-4.6903698331
0.6%
-4.6989700041
0.6%

log10NH
Real number (ℝ≥0)

Distinct150
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.57772856
Minimum20.51188336
Maximum22.7512791
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2021-10-30T12:15:00.297695image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20.51188336
5-th percentile20.82621607
Q121.24762992
median21.59160359
Q321.92086639
95-th percentile22.38542387
Maximum22.7512791
Range2.239395743
Interquartile range (IQR)0.6732364728

Descriptive statistics

Standard deviation0.4758184814
Coefficient of variation (CV)0.02205137024
Kurtosis-0.5491850026
Mean21.57772856
Median Absolute Deviation (MAD)0.3366352281
Skewness0.01106687156
Sum3711.369312
Variance0.2264032273
MonotonicityNot monotonic
2021-10-30T12:15:00.400249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.017033344
 
2.3%
22.017033343
 
1.7%
21.419955753
 
1.7%
21.348304863
 
1.7%
21.916980052
 
1.2%
21.620136052
 
1.2%
21.32837962
 
1.2%
21.350248022
 
1.2%
21.413299762
 
1.2%
21.496929652
 
1.2%
Other values (140)147
85.5%
ValueCountFrequency (%)
20.511883361
0.6%
20.535294121
0.6%
20.642464521
0.6%
20.657055851
0.6%
20.672097861
0.6%
20.719331291
0.6%
20.726727211
0.6%
20.792391691
0.6%
20.806179971
0.6%
20.842609241
0.6%
ValueCountFrequency (%)
22.75127911
0.6%
22.567026371
0.6%
22.562292861
0.6%
22.499687081
0.6%
22.498310551
0.6%
22.460897841
0.6%
22.442479771
0.6%
22.419955751
0.6%
22.387389831
0.6%
22.383815371
0.6%

log10z
Real number (ℝ)

HIGH CORRELATION

Distinct164
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2012252139
Minimum-1.096910013
Maximum0.9731278536
Zeros0
Zeros (%)0.0%
Negative44
Negative (%)25.6%
Memory size1.5 KiB
2021-10-30T12:15:00.506063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1.096910013
5-th percentile-0.4513142987
Q1-0.012892644
median0.2446479958
Q30.4565527064
95-th percentile0.6568852603
Maximum0.9731278536
Range2.070037867
Interquartile range (IQR)0.4694453504

Descriptive statistics

Standard deviation0.3575272558
Coefficient of variation (CV)1.776751774
Kurtosis1.617335286
Mean0.2012252139
Median Absolute Deviation (MAD)0.223773031
Skewness-1.011433982
Sum34.6107368
Variance0.1278257386
MonotonicityNot monotonic
2021-10-30T12:15:00.608851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3617278362
 
1.2%
0.61278385672
 
1.2%
-0.086186147622
 
1.2%
0.19033169822
 
1.2%
0.55630250082
 
1.2%
0.32221929472
 
1.2%
0.15836249212
 
1.2%
-0.096910013012
 
1.2%
0.28981183911
 
0.6%
-0.13966199341
 
0.6%
Other values (154)154
89.5%
ValueCountFrequency (%)
-1.0969100131
0.6%
-0.95467702121
0.6%
-0.93181413831
0.6%
-0.9030899871
0.6%
-0.83120797971
0.6%
-0.6601512171
0.6%
-0.52724355071
0.6%
-0.46054750851
0.6%
-0.45469288351
0.6%
-0.4485500021
0.6%
ValueCountFrequency (%)
0.97312785361
0.6%
0.91381385241
0.6%
0.7481880271
0.6%
0.69897000431
0.6%
0.69810054561
0.6%
0.690196081
0.6%
0.6787004351
0.6%
0.67274419831
0.6%
0.67209785791
0.6%
0.64443858951
0.6%

Interactions

2021-10-30T12:14:56.497933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:44.331271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:45.407037image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:46.314164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:47.382049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:48.384846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:49.442793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:50.481251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:51.519954image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:52.470966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:53.399205image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:54.463185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:55.495329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:56.565015image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:44.449272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:45.473684image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:46.387916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:47.449795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:48.457017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:49.513456image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:50.551922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:51.590086image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:52.536450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:53.471789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:54.533157image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:55.562883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:56.630417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:44.516753image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:45.537591image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:46.459620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:47.515248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:48.527226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:49.582047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:50.621276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:51.658162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:52.683049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:53.542267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:54.601629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:55.628408image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:56.706124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:44.594716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:45.612364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:46.622163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:47.591539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:48.607858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:49.661241image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:50.700573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:51.736795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:52.748360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:53.623435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:54.680093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:55.704608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:56.774421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:44.665385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:45.679636image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:46.696255image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:47.659942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:48.680877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:49.732850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:50.772457image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:51.808321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:52.813591image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:53.697538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:54.751229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:55.773083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:56.848710image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:44.741745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:45.753270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:46.775980image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:47.814382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:48.759717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:49.810617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:50.850078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:51.885542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:52.879483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:53.859250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:54.828731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:55.847678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:56.921931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:44.817519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:45.825560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:46.854748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:47.888387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:48.837356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:49.886764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:50.926431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:51.961407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:52.944336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:53.938111image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:54.904832image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:55.921131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:56.994948image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:44.892687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:45.898003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:46.933361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:47.962102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:48.915196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:49.963396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:51.002524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:52.037437image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:53.009016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:54.016758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:55.062005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:55.994634image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:57.066307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:44.966295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:45.968844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:47.010609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:48.034058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:49.073469image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:50.038307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:51.077659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:52.111427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:53.074239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:54.093649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:55.136399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:56.066400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:57.130893image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:45.033229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:46.034688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:47.075763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:48.099533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:49.139896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:50.102987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:51.142336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:52.176779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:53.139163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:54.158576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:55.201586image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:56.131693image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:57.206238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:45.110558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:46.109143image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:47.156894image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:48.175661image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:49.220453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:50.262791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:51.221504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:52.255102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:53.204118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:54.239343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:55.279839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:56.288964image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:57.277414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:45.183677image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:46.179703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:47.234205image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:48.247706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:49.296708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:50.338000image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:51.296316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:52.329185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:53.269431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:54.316210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:55.353513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:56.361228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:57.345924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:45.253641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:46.247052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:47.308264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:48.316337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:49.369899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:50.409794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:51.448113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:52.400245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:53.334259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:54.389776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:55.424650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-30T12:14:56.429579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-10-30T12:15:00.709036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-30T12:15:00.862216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-30T12:15:01.015139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-30T12:15:01.249763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-30T12:14:57.571567image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-30T12:14:57.748623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Unnamed: 0FbestT_abestAlpha_bestPhotonindex_plateauBetaGammalog10PeakFluxlog10Epeaklog10T90log10Fluencelog10NHlog10z
00-11.375704.863561.1081801.85600.85601.99544-6.950782-inf1.980458-5.49214421.9169800.289812
11-10.704703.739970.8631591.97100.97102.01045-7.112383-inf2.183270-5.88272920.9375180.510545
22-9.653993.124350.9841081.99950.99951.78711-5.958607-inf1.522444-5.08512822.1271050.462398
33-10.556402.994810.8422171.90000.90002.06183-6.653647-inf0.397940-6.43533421.751279-0.184754
44-10.098404.050182.3876301.56150.56151.61770-7.337242-inf2.194514-5.62342321.7387810.598791
55-10.286903.639731.4472901.85850.85851.85570-6.655608-inf1.278754-5.69897021.2576790.233047
66-10.860204.217211.5223402.21801.21802.11696-7.088842-inf1.943989-5.66756221.3483050.544068
77-12.238805.085580.9544811.86000.86001.94709-7.488117-inf1.354108-6.57511820.970812-0.080922
88-12.333504.762351.8010002.11001.11002.45742-7.498941-inf1.550228-6.38405021.960946-0.527244
99-11.589604.257421.1477101.73000.73001.89482-7.125518-inf0.602060-6.76955121.714330-0.028539

Last rows

Unnamed: 0FbestT_abestAlpha_bestPhotonindex_plateauBetaGammalog10PeakFluxlog10Epeaklog10T90log10Fluencelog10NHlog10z
162196-11.677604.784111.9790101.8370.8371.91015-6.197226-inf2.176091-5.08092220.8697410.205299
163197-10.752703.606980.9989471.8300.8301.88453-6.600326-inf1.778151-5.39794021.743124-0.161781
164198-10.924404.020891.8690401.9150.8202.03508-6.609065-inf2.468347-5.11350921.6617690.322219
165199-11.760003.446191.4245201.6300.6301.46604-7.163676-inf1.806180-5.92081922.1306940.585461
166200-10.167303.529241.3725201.5300.5301.88559-6.453457-inf1.824126-5.23657221.7777100.389520
167202-9.764992.566061.0954601.8600.8602.05668-6.062482-inf-0.091515-6.05551721.6081430.113943
168203-9.872832.775611.4206602.1001.1001.80565-6.090444-inf0.857935-5.58502721.4857440.120409
169204-9.792963.118871.4765501.9800.9801.94118-6.301899-inf2.013259-5.07935521.6536330.173186
170205-10.665502.646571.0042901.7050.6402.03664-6.806875-inf0.079181-6.74472721.3117540.416474
171206-11.097703.587091.4064901.8600.8601.93407-6.928118-inf1.365488-5.92081920.9014580.380211